Evidence map›Paper›PMID 40064176›Full record

ArticleMedical education2025

Artificial intelligence and gender equity: An integrated approach for health professional education.

Margaret Bearman, Rola Ajjawi

Abstract read
In one paragraph

Article in Medical education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Margaret BearmanCentre for Research in Assessment and Digital Learning (CRADLE), Deakin University, Melbourne, Victoria, Australia.ORCID 0000-0002-6862-9871
Rola AjjawiCentre for Health Education Scholarship, Department of Surgery, University of British Columbia, Vancouver, Canada.ORCID 0000-0003-0651-3870

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionAs artificial intelligence (AI) increasingly integrates into health workplaces, evidence suggests AI can exacerbate gender inequity. Health professional programmes have a role to play in ensuring graduates grasp the challenges facing working in an AI-mediated world. APPROACH: Drawing from feminist scholars and empirical evidence, this conceptual paper synthesises current and future ways in which AI compounds gender inequities and, in response, proposes foci for an integrated approach to teaching about AI and equity. ANALYSIS: We propose three concerns. Firstly, multiple literature reviews suggest that the gender divide is embedded within AI technologies from both process (AI development) and product (AI output) perspectives. Next, there is emerging evidence that AI is reinforcing already entrenched health workforce inequities, where certain types of roles are seen as being the domain of certain genders. Finally, AI may disassociate health professionals' interactions with an embodied, agentic patient by diverting attention to a gendered digital twin. IMPLICATIONS: Responding to these concerns is not simply a matter of teaching about bias but needs to promote an understanding of AI as a sociotechnical phenomenon. Healthcare curricula could usefully provide clinically relevant educational experiences that illustrate how AI intersects with inequitable gendered knowledge practices. Students can be directed to: (1) explore doubts when working with AI-generated data or decisions; (2) refocus on caring through prioritising embodied connections; and (3) consider how to negotiate gendered workplaces in a time of AI.

conclusionThe intersection of gender equity and AI provides an accessible, illustrative case about how changing knowledge practices have the potential to embed inequity and how health professional education programmes might respond.

Indexed as

Artificial IntelligenceGender EquityHealth PersonnelFemaleHumansMaleSexism

Identifiers

PMID40064176
PMCPMC12437998

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.